In the oil and gas industry, modeling of the subsurface is typically utilized for visualization and to assist with analyzing the subsurface volume for potential locations of hydrocarbon resources, well planning for field deployment, and development plans for producing from a reservoir. Accordingly, various methods exist for estimating properties of subsurface volumes which are then used to model and simulate the subsurface volume. However, reservoir modeling and/or simulation is significantly more challenging for fractured reservoirs than it is for conventional un-fractured reservoirs.
Many fine-grained reservoirs (e.g., clastics, carbonates, and mudrocks) require additional permeability associated with partially open natural fractures in order to achieve economic flow rates. Thus, it is important to be able to accurately model and simulate a fractured reservoir to determine if economic flow rates are achievable. However, while the impact of depletion-driven matrix compaction is often accounted for when simulating the performance of conventional reservoirs, such as deep-water sands, the ability to predict in a similar fashion the impact of declining fluid pressure on the productivity of a fractured reservoir, particularly a naturally fractured reservoir, has been a challenge. Thus, there remains a need for a method to predict and model the changes in a fractured reservoir.
Previously the dual porosity approach has been used to simulate fractured reservoirs. See Warren et al., “The Behavior of Naturally Fractured Reservoirs”, Society of Petroleum Engineers, doi 10:2118/426-PA (1963). This approach utilizes an interacting continuum to reflect storage and permeability characteristics of a natural fracture network. However, the dual porosity approach does not adequately address connectivity, which is generally better addressed by discrete fracture network (DFN) models. See Dershowitz et al. “Discrete Fracture Approaches for Oil and Gas Applications,” Proceedings from the North American Rock Mechanics Symposium, Austin, Tex. (1994).
A representation of a conventional method for linking dual porosity and DFN models is shown in FIG. 1. In general, grid cells and properties, such as lithology, that are associated with each cell are exported from a geologic model 100 into a DFN model 110. A representation of the natural fracture system is then generated within the DFN model 110 utilizing techniques that use data from a number of sources, such as core, outcrop, well, and seismic data. The DFN model 110 can typically incorporate multiple fracture sets each having spatial distributions of fracture intensities, sizes (lengths and apertures), and orientations. The DFN model 110 is then used to determine: (i) fracture system porosity, which is typically calculated as the product of fracture intensities expressed as fracture area per unit volume and fracture apertures; (ii) directional fracture system permeabilities in the x-, y-, and z-directions, which are typically calculated using tensor approaches in which the equivalent porous medium properties of each grid cell are dependent on the fracture intensity, connectivity of the fracture network, and distribution of fracture transmissivities, see Oda, “Permeability Tensor for Discontinuous Rock Masses”, Géotechnique, 34(4), pp 483-495 (1985), and; (iii) fracture spacing/matrix block size in the x-, y-, and z-directions, which provide a measure of the accessibility of the matrix system through the fracture system and is related to various aspects of the fracture system such as orientation, number of fracture sets, and intensity. Fracture system porosity, directional fracture system permeabilities, and matrix block sizes are then exported from the DFN model 110 into the dual porosity reservoir simulator 120. The simulator 120 can then be history matched 130 with well testing data (e.g., permeability thickness or productivity index) in order to achieve a good calibration with field production data. Once calibrated, the simulator 120 may then be run in a forecast mode to predict the performance of the reservoir 140.
A major shortcoming of the approach for incorporating natural fractures into reservoir simulations that is illustrated in FIG. 1 is that all directional fracture system permeabilities and fracture system porosities are necessarily “static” values. That is, the fracture system permeabilities and fracture system porosities constitute a single representation of the fracture system at a particular state of stress, which is the unstressed condition associated with the outcrop or core description of fracture geometric properties, the initial reservoir stress conditions associated with the history matching of the well data prior to production, or some combination of the two. As such, the model is not able to account for the dynamic character of a compressible fracture system in which progressive closure of the fracture apertures due to production-induced drawdown and/or depletion of reservoir pressure can have a first order influence on the performance prediction of the reservoir over time.
Thus, there remains a need for an improved method and system for modeling and simulating a fractured reservoir and predicting reservoir performance. In particular, there remains a need for a method and system that can account for the impact on reservoir performance due to the progressive closure of fractures with declining fluid pressure.